code: def forward(self, predicts, batch):
      self.total_loss = {}
      total_loss = 0.0
      if isinstance(predicts, tuple) or isinstance(predicts, list):
          cnt_outputs, seq_outputs = predicts
      else:
          cnt_outputs, seq_outputs = predicts, None
      # batch [image, label, length, cnt_label]
      if cnt_outputs is not None:
          cnt_loss = self.cnt_loss(
              cnt_outputs, paddle.cast(batch[3], paddle.float32)
          )
          self.total_loss["cnt_loss"] = cnt_loss
          total_loss += cnt_loss
  
      if seq_outputs is not None:
          targets = batch[1].astype("int64")
          label_lengths = batch[2].astype("int64")
          batch_size, num_steps, num_classes = (
              seq_outputs.shape[0],
              seq_outputs.shape[1],
              seq_outputs.shape[2],
          )
          assert (
              len(targets.shape) == len(list(seq_outputs.shape)) - 1
          ), "The target's shape and inputs's shape is [N, d] and [N, num_steps]"
  
          inputs = seq_outputs[:, :-1, :]
          targets = targets[:, 1:]
  
          inputs = paddle.reshape(inputs, [-1, inputs.shape[-1]])
          targets = paddle.reshape(targets, [-1])
          seq_loss = self.seq_loss(inputs, targets)
          self.total_loss["seq_loss"] = seq_loss
          total_loss += seq_loss
  
      self.total_loss["loss"] = total_loss
      return self.total_loss
  
file_path: PaddleOCR/ppocr/losses/rec_rfl_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  from .basic_loss import CELoss, DistanceLoss
label_desc: Use image node_id 2 for calling the RFLLoss obj's underlying member method code with example usage: obj.forward(predicts, batch) and returns: self